Spectrum Extraction

The specreduce.extract module extracts a 1D spectrum from an input 2D spectrum (likely a background-extracted spectrum from the previous step) and a defined window, using one of the following implemented methods:

• BoxcarExtract

• HorneExtract

Each of these takes the input image and trace as inputs (see the API Reference for other required and optional parameters)

extract = specreduce.extract.BoxcarExtract(image-bg, trace, width=3)

or

extract = specreduce.extract.HorneExtract(image-bg, trace)

For the Horne algorithm, the variance array is required. If the input image is an NDData object with image.uncertainty provided, then this will be used. Otherwise, the variance parameter must be set.

extract = specreduce.extract.HorneExtract(image-bg, trace, variance=var_array)

An optional mask array for the image may be supplied to HorneExtract as well. This follows the same convention and can either be attached to image if it is an NDData object, or supplied as a keyword argument.

The extraction methods automatically detect non-finite pixels in the input image and combine them with the user-supplied mask to prevent them from biasing the extraction. In the boxcar extraction, the treatment of these pixels is controlled by the mask_treatment option. When set to exclude (the default), non-finite pixels within the extraction window are excluded from the extraction, and the extracted flux is scaled according to the effective number of unmasked pixels. When using other options (filter or omit), the non-finite values may be propagated or treated differently as documented in the API.

The previous examples in this section show how to initialize the BoxcarExtract or HorneExtract objects with their required parameters. To extract the 1D spectrum

spectrum = extract.spectrum

The extract object contains all the set options. The extracted 1D spectrum can be accessed via the spectrum property or by calling (e.g extract()) the extract object (which also allows temporarily overriding any values)

spectrum2 = extract(width=6)

or, for example to override the original trace_object

spectrum2 = extract(trace_object=new_trace)

Spatial profile options

The Horne algorithm provides two options for fitting the spatial profile to the cross dispersion direction of the source: a Gaussian fit (default), or an empirical interpolated_profile option.

If the default Gaussian option is used, an optional background model may be supplied as well (default is a 2D Polynomial) to account for residual background in the spatial profile. This option is not supported for interpolated_profile.

If the interpolated_profile option is used, the image will be sampled in various wavelength bins (set by n_bins_interpolated_profile), averaged in those bins, and samples are then interpolated between (linear by default, interpolation degree can be set with interp_degree_interpolated_profile, which defaults to linear in x and y) to generate an empirical interpolated spatial profile. Since this option has two optional parameters to control the fit, the input can either be a string to indicate that interpolated_profile should be used for the spatial profile and to use the defaults for bins and interpolation degree, or to override these defaults a dictionary can be passed in.

For example, to use the interpolated_profile option with default bins and interpolation degree

interp_profile_extraction = extract(spatial_profile='interpolated_profile')

Or, to override the default of 10 samples and use 20 samples

interp_profile_extraction = extract(spatial_profile={'name': 'interpolated_profile',
                                'n_bins_interpolated_profile': 20)

Or, to do a cubic interpolation instead of the default linear

interp_profile_extraction = extract(spatial_profile={'name': 'interpolated_profile',
                                'interp_degree_interpolated_profile': 3)

As usual, parameters can either be set when instantiating the HorneExtraxt object, or supplied/overridden when calling the extraction method on that object.